ALL-E: Aesthetics-guided Low-light Image Enhancement

Ling Li ¹, Dong Liang¹, Yuanhang Gao ¹, Sheng-Jun Huang ¹, Songcan Chen ¹

¹ Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Collaborative Innovation Center of Novel Software Technology and Industrialization

Abstract

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm,i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods. Source code and models are in the supplementary material for evaluation.

Overall architecture of our proposed ALL-E. We devise aesthetics-guided LLE (ALL-E) through reinforcement learning and treat LLE as a Markov decision process, which divides the LLE process into two phases: aesthetic policy generation and aesthetic assessment.

Contributions

We propose a new paradigm for LLE by integrating aesthetic assessment, leveraging aesthetic scores to mimic human subjective evaluations as a reward to guide LLE. To the best of our knowledge, this is the first attempt to solve low-light image enhancement using aesthetics
We devise aesthetics-guided LLE (ALL-E) through reinforcement learning and treat LLE as a Markov decision process, which divides the LLE process into two phases: aesthetic policy generation and aesthetic assessment.
Our method is evaluated against state-of-the-art competitors through comprehensive experiments in terms of visual quality, no- and full-referenced image quality assessment, and human subjective surveys. All results consistently demonstrate the superiority of ALL-E.

Results

Visual quality comparison

Image quality assessment

Ablation Studies

Materials

PDF| Homepage | Code

Citation

@article{li2023alle,
      title = {ALL-E: Aesthetics-guided Low-light Image Enhancement},
      author ={Ling Li and Dong Liang and Yuanhang Gao and Sheng-Jun Huang and Songcan Chen},
      booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}},
      publisher = {International Joint Conferences on Artificial Intelligence Organization},
      year={2023},
      month = {8},
      pages = {1062--1070}
}

Contact

If you have any questions, please contact Dong Liang at liangdong@nuaa.edu.cn or gaoyuanhang@nuaa.edu.cn.